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Search Results: 1 - 10 of 139560 matches for " K. Raja "
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A BP Artificial Neural Network Model for Earthquake Magnitude Prediction in Himalayas, India  [PDF]
S. Narayanakumar, K. Raja
Circuits and Systems (CS) , 2016, DOI: 10.4236/cs.2016.711294
Abstract: The aim of this study is to evaluate the performance of BP neural network techniques in predicting earthquakes occurring in the region of Himalayan belt (with the use of different types of input data). These parameters are extracted from Himalayan Earthquake catalogue comprised of all minor, major events and their aftershock sequences in the Himalayan basin for the past 128 years from 1887 to 2015. This data warehouse contains event data, event time with seconds, latitude, longitude, depth, standard deviation and magnitude. These field data are converted into eight mathematically computed parameters known as seismicity indicators. These seismicity indicators have been used to train the BP Neural Network for better decision making and predicting the magnitude of the pre-defined future time period. These mathematically computed indicators considered are the clustered based on every events above 2.5 magnitude, total number of events from past years to 2014, frequency-magnitude distribution b-values, Gutenberg-Richter inverse power law curve for the n events, the rate of square root of seismic energy released during the n events, energy released from the event, the mean square deviation about the regression line based on the Gutenberg-Richer inverse power law for the n events, coefficient of variation of mean time and average value of the magnitude for last n events. We propose a three-layer feed forward BP neural network model to identify factors, with the actual occurrence of the earthquake magnitude M and other seven mathematically computed parameters seismicity indicators as input and target vectors in Himalayan basin area. We infer through comparing curve as observed from seismometer in Himalayan Earthquake catalogue comprised of all events above magnitude 2.5 mg, their aftershock sequences in the Himalayan basin of year 2015 and BP neural network predicting earthquakes in 2015. The model yields good prediction result for the earthquakes of magnitude between 4.0 and 6.0.
Reproductive Performance and Fiber Quality Responses of Cotton to Potassium Nutrition  [PDF]
Suresh Lokhande, K. Raja Reddy
American Journal of Plant Sciences (AJPS) , 2015, DOI: 10.4236/ajps.2015.67099
Abstract: Potassium (K) deficiency affects cotton growth and development and fiber properties. An experiment was conducted in an outdoor pot culture facility by imposing four potassium stress treatments (100%, 40%, 20% and 0% of optimum K level) prior to flowering during 2010 and 2011 growing season. Upland cotton cultivar, TM-1, was seeded in the pots comprised of fine sand as rooting medium. Flowers and bolls were tagged daily to estimate boll maturation period (BMP). Leaf samples were collected every four days from flowering to maturity to estimate leaf K content. Plant height and node numbers were recorded from emergence to 21 days after treatment. Photosynthesis and stomatal conductance were measured weekly from day of treatment imposition to physiological maturity at an interval of seven days. Stem, leaf, and boll dry-component weights, and boll numbers were recorded at the end of the experiment in each year. From each boll, the lint samples were collected and grouped based on average leaf potassium concentration during BMP, and fiber quality parameters were recorded for each group in each treatment. At high K deficient (0 K) condition, total biomass declined by 27% and 28% in years 2010 and 2011, respectively. Significantly, lower numbers of bolls were retained per plant at 0 K stress treatment during both the years. Leaf photosynthesis (r2 = 0.92) and stomatal conductance (r2 = 0.80) declined with declining leaf K levels. Fiber length, strength, micronaire, and uniformity declined linearly with decrease in leaf K content. Weaker fibers with medium length were produced under K-deficient conditions with micronaire values in the discount range. Fiber uniformity, however, did not decline with decrease in leaf K. The identified leaf K status-specific relationships for fiber properties could be used to improve management practices under potassium deficiency and to develop new sub-routines of the existing cotton simulation models. New and improved models will be useful not only in management, but also in arena of policy decisions including future climate change impact assessment analysis.
Studies on the Growth and Characterization of L-Arginine Maleate Dihydrate Crystal Grown from Liquid Diffusion Technique  [PDF]
K. Ramya, C. Ramachandra Raja
Journal of Minerals and Materials Characterization and Engineering (JMMCE) , 2016, DOI: 10.4236/jmmce.2016.42014
Abstract: Nonlinear optical crystals of L-Arginine maleate dihydrate were grown from liquid diffusion method. The lattice parameters of the crystal were identified using single crystal and powder crystal X-ray diffraction analyses. Fourier transform infrared spectroscopy and Fourier transform Raman spectroscopy were made to study the vibrational functional groups in the grown crystal. Optical absorption and transmission ranges were measured from UV-VIS-NIR spectrum. The molecular structure of the crystal is established through 1H-NMR and 13C-NMR studies. Thermal stabilities and decomposition of the grown crystal were studied from TG/DTA and DSC analyses. Nonlinear optical property of the crystal was determined by Kurtz and Perry powder technique.
Elements of Magical Rituals: A Traditional Practice of North Kerala  [PDF]
P. Vijisha, E. K. Govinda Varma Raja
Open Journal of Social Sciences (JSS) , 2015, DOI: 10.4236/jss.2015.311034
Abstract: The strengthening of mental power will help the patients to overcome their unrest. Mental unrest of an individual is the product of the folk which he/she belongs. The folk itself have found out the remedy for it as traditional rituals. The traditional practitioners have developed certain tools and technologies with the help of indigenous knowledge. It was examined with the case studies practised in north Kerala.
Existence of Untouchability towards Maari Theyyam
—A Traditional Art Form of Kerala
 [PDF]

P. Vijisha, E. K. Govinda Varma Raja
Open Journal of Social Sciences (JSS) , 2016, DOI: 10.4236/jss.2016.43032
Abstract: The renaissance movement led by Vaikundam Swamikal, Sreekumara Gurudevan, Ayyankali, Chattambi Swamikal, Dr. V. V. Velukkutty Arayan, Sahodharan Ayyappan, Swami Vakbhadananda and Swami Anantha Theertha was helping to eradicate untouchability from Kerala. The Temple entry proclamation by the erstwhile king of Travancore late Sri Chitra Thirunal Rama Varma Maharaja on his 24th birthday (12th November 1936) proclaimed that all Hindus by birth or faith, despite their caste will be allowed enter to all temples under the governance of Travancore state. The Temple Madayi Kavu1 is traditionally owned by Kolathiri King of Chirakkal. But it is controlled by the Malabar Devaswam Board, an authorised body of Government of Kerala. The goddess of this temple is the family deity of Travancore royal family. Maari Theyyam is a traditional ritual performance of Madayi Kavu. This ritual is not allowed to be observed in the premises of Madayi Kavu. All other Theyyams are allowed to be performed in the premises of the temple. Maari Theyyam is performed away from the temple premises. It is a kind of untouchability towards Maari Theyyam. Here, we try to examine the pros and cons of the untouchability of the ritual Maari Theyyam.
Alleviating the Cold Start Problem in Recommender Systems Based on Modularity Maximization Community Detection Algorithm  [PDF]
S. Vairachilai, M. K. Kavithadevi, M. Raja
Circuits and Systems (CS) , 2016, DOI: 10.4236/cs.2016.78111
Abstract: Recommender system (RS) has become a very important factor in many eCommerce sites. In our daily life, we rely on the recommendation from other persons either by word of mouth, recommendation letters, movie, item and book reviews printed in newspapers, etc. The typical Recommender Systems are software tools and techniques that provide support to people by identifying interesting products and services in online store. It also provides a recommendation for certain users who search for the recommendations. The most important open challenge in Collaborative filtering recommender system is the cold start problem. If the adequate or sufficient information is not available for a new item or users, the recommender system runs into the cold start problem. To increase the usefulness of collaborative recommender systems, it could be desirable to eliminate the challenge such as cold start problem. Revealing the community structures is crucial to understand and more important with the increasing popularity of online social networks. The community detection is a key issue in social network analysis in which nodes of the communities are tightly connected each other and loosely connected between other communities. Many algorithms like Givan-Newman algorithm, modularity maximization, leading eigenvector, walk trap, etc., are used to detect the communities in the networks. To test the community division is meaningful we define a quality function called modularity. Modularity is that the links within a community are higher than the expected links in those communities. In this paper, we try to give a solution to the cold-start problem based on community detection algorithm that extracts the community from the social networks and identifies the similar users on that network. Hence, within the proposed work several intrinsic details are taken as a rule of thumb to boost the results higher. Moreover, the simulation experiment was taken to solve the cold start problem.
Information Driven Data Gathering for Energy Efficient Wireless Sensor Network  [PDF]
K. Anna Raja, L. R. Karlmarx
Circuits and Systems (CS) , 2016, DOI: 10.4236/cs.2016.711324
Abstract: Large scale dense Wireless Sensor Networks (WSNs) have been progressively employed for different classes of applications for the resolve of precise monitoring. As a result of high density of nodes, both spatially and temporally correlated information can be detected by several nodes. Hence, energy can be saved which is a major aspect of these networks. Moreover, by using these advantages of correlations, communication and data exchange can be reduced. In this paper, a novel algorithm that selects the data based on their contextual importance is proposed. The data, which are contextually important, are only transmitted to the upper layer and the remains are ignored. In this way, the proposed method achieves significant data reduction and in turn improves the energy conservation of data gathering.
Current literature: Dermatology 2001-2002
Raja Babu K
Indian Journal of Dermatology, Venereology and Leprology , 2003,
Abstract:
Sexually transmitted infections
Raja Babu K
Indian Journal of Dermatology, Venereology and Leprology , 2005,
Abstract:
30th National conference of the Indian Association of Dermatologists, Venereologists and Leprologists, 24-27 January, 2002. Cochin, Kerala.
Raja Babu K
Indian Journal of Dermatology, Venereology and Leprology , 2002,
Abstract:
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